Recent development and biomedical applications of probabilistic Boolean networks

Cell Communication and Signaling

Volumn 11, Issue 1, 2013, Pages

  Trairatphisan, Panuwat ^a   Mizera, Andrzej ^a   Pang, Jun ^a   Tantar, Alexandru Adrian ^a   Schneider, Jochen ^a,b   Sauter, Thomas ^a  

^a UNIVERSITY OF LUXEMBOURG   (Luxembourg)

^b SAARLAND UNIVERSITY   (Germany)

Author keywords

Probabilistic Boolean networks; Probabilistic graphical models; Qualitative modelling; Systems biology

Indexed keywords

JANUS KINASE; STAT PROTEIN;

ALGORITHM; BAYES THEOREM; BAYESIAN NETWORK; BIOMEDICINE; BOOLEAN NETWORK; BOOLEAN NETWORK WITH PERTURBATION; COMPARATIVE STUDY; CONTROL; DATA ANALYSIS; DYNAMIC BAYESIAN NETWORK; EPIGENETICS; FINITE HORIZON EXTERNAL CONTROL; GENE FUNCTION; GENE REGULATORY NETWORK; GENETIC MODEL; HUMAN; INFINITE HORIZON EXTERNAL CONTROL; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MATHEMATICAL PHENOMENA; NETWORK INFERENCE; NONHUMAN; PHYSIOLOGY; PRIORITY JOURNAL; PROBABILISTIC BOOLEAN NETWORK; PROBABILISTIC BOOLEAN NETWORK WITH PERTURBATION; PROBABILISTIC REGULATION OF METABOLISM ALGORITHM; PROBABILITY; REVIEW; SIGNAL TRANSDUCTION; STEADY STATE; STRUCTURAL INTERVENTION; SYSTEMS BIOLOGY; TIME SERIES ANALYSIS; BIOLOGICAL MODEL; STATISTICAL MODEL;

GENE REGULATORY NETWORKS; HUMANS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PROBABILITY;

EID: 84879481411     PISSN: None     EISSN: 1478811X     Source Type: Journal    
DOI: 10.1186/1478-811X-11-46     Document Type: Review

References (165)

1. Addressing parameter identifiability by model-based experimentation. Raue A, Kreutz C, Maiwald T, Klingmueller U, Timmer J, IET Syst Biol 2010 5 2 120 130
2. Simulating quantitative cellular responses using asynchronous threshold Boolean network ensembles. Jack J, Wambaugh J, Shah I, BMC Syst Biol 2011 5 1 1 13 10.1186/1752-0509-5-1 21194489
3. From Boolean to probabilistic Boolean networks as models of genetic regulatory networks. Shmulevich I, Dougherty ER, Zhang W, Proc of the IEEE 2002 90 11 1778 1792 10.1109/JPROC.2002.804686
4. Metabolic stability and epigenesis in randomly constructed genetic nets. Kauffman SA, J Theor Biol 1969 22 3 437 467 10.1016/0022-5193(69)90015-0 5803332
5. Homeostasis and differentiation in random genetic control networks. Kauffman SA, Nature 1969 224 177 178 10.1038/224177a0 5343519
6. The large scale structure and dynamics of gene control circuits: an ensemble approach. Kauffman SA, J Theor Biol 1974 44 167 190 10.1016/S0022-5193(74)80037-8 4595774
7. Kauffman SA, The Origins of Order: Self-Organization and Selection in Evolution Oxford: Oxford University Press 1993
8. Shmulevich I, Dougherty ER, Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks Philadelphia PA: SIAM Press 2010
9. Finding intervention points in the pathogenesis of dengue viral infection. Tay J, Tan P, Proc. 28th IEEE EMBS Annual International Conference: 30 August-3 September; New York City, NY, USA IEEE Computer Society 2006 5315 5321 (Pubitemid 46521115)
10. Probabilistic Boolean network analysis of brain connectivity in Parkinsons disease. Ma Z, Wang Z, McKeown M, IEEE J Selected Topics in Signal Process 2008 2 6 975 985
11. Steady-state analysis of genetic regulatory networks modelled by probabilistic Boolean networks. Shmulevich I, Gluhovsky I, Hashimoto RF, Dougherty ER, Zhang W, Comp and Funct Genomics 2003 4 601 608 10.1002/cfg.342 (Pubitemid 38088670)
12. An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks. Ching W, Zhang S, Ng M, Akutsu T, Bioinformatics 2007 23 12 1511 1518 10.1093/bioinformatics/btm142 17463027 (Pubitemid 47244476)
13. On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation. Li W, Cui L, Ng MK, J Comput Appl Math 2012 236 4067 4081 10.1016/j.cam.2012.02.022
14. Growing genetic regulatory networks from seed genes. Hashimoto R, Kim S, Shmulevich I, Zhang W, Bittner M, Dougherty E, Bioinformatics 2004 20 8 1241 1247 10.1093/bioinformatics/bth074 14871865 (Pubitemid 38807580)
15. Generating probabilistic Boolean networks from a prescribed transition probability matrix. Ching W, Chen X, Tsing N, IET Syst Biol 2009 3 6 453 464 10.1049/iet-syb.2008.0173 19947771
16. Inference of Boolean networks under constraint on bidirectional gene relationships. Vahedi G, Ivanov I, Dougherty E, IET Syst Biol 2009 3 3 191 202 10.1049/iet-syb.2007.0070 19449979
17. Intervention in context-sensitive probabilistic Boolean networks. Pal R, Datta A, Bittner ML, Dougherty ER, Bioinformatics 2005 21 7 1211 1218 10.1093/bioinformatics/bti131 15531600 (Pubitemid 40467947)
18. Gene perturbation and intervention in probabilistic Boolean networks. Shmulevich I, Dougherty ER, Zhang W, Bioinformatics 2002 18 10 1319 1331 10.1093/bioinformatics/18.10.1319 12376376 (Pubitemid 35244189)
19. An optimal control approach to probabilistic Boolean networks. Liu Q, Physica A 2012 391 6682 6689 10.1016/j.physa.2012.07.074
20. Reachability analysis of probabilistic Boolean networks using model checking. Kobayashi K, Hiraishi K, Proc. SICE Annual Conference: 18-21 August; Taipei, Taiwan IEEE Computer Society 2010 829 832
21. Comparative study on sensitivities of Boolean networks. Qian X, Dougherty E, Proc. IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS): 10-12 November; Cold Spring Harbor, NY, USA IEEE Computer Society 2010 1 4
22. Modelling of macrophage gene expression in the interferon pathway. Yu L, Marshall S, Forster T, Ghazal P, Proc. IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS): 28-30 May; College Station, Texas, USA IEEE Computer Society 2006 45 46
23. Budding yeast cell cycle modeled by context-sensitive probabilistic Boolean network. Hashimoto R, Stagni H, Higa C, Proc. IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS): 17-21 May; Minneapolis, MN, USA 2009 1 4
24. A stochastic model of epigenetic dynamics in somatic cell reprogramming. Flöttmann M, Scharp T, Klipp E, Front Physiol 2012 3 216 1 19 22275902
25. Hybrid modeling of cell signaling and transcriptional reprogramming and its application in C.elegans development. Fertig E, Danilova L, Favorov A, Ochs M, Front Genet 2011 2 77 1 9 22303300
26. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Chandrasekaran S, Price N, Proc Nat Acad Sci 2010 107 41 17845 17850 10.1073/pnas.1005139107 20876091
27. BN/PBN Toolbox. [ http://code.google.com/p/pbn-matlab-toolbox ]
28. ADAM: analysis of discrete models of biological systems using computer algebra. Hinkelmann F, Brandon M, Guang B, McNeill R, Blekherman G, Veliz-Cuba A, Laubenbacher R, BMC Bioinformatics 2011 12 295 10.1186/1471-2105-12-295 21774817
29. Boolnet: an R package for generation, reconstruction and analysis of Boolean networks. Müssel C, Hopfensitz M, Kestler H, Bioinformatics 2010 20 10 1378 1380
30. Norris JR, Markov Chains Cambridge UK: Cambridge University Press 1998
31. Steady-state probabilities for attractors in probabilistic Boolean networks. Brun M, Dougherty ER, Shmulevich I, Signal Process 2005 85 1993 2013 10.1016/j.sigpro.2005.02.016 (Pubitemid 41260387)
32. Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Shmulevich I, Dougherty ER, Kim S, Zhang W, Bioinformatics 2002 18 2 261 274 10.1093/bioinformatics/18.2.261 11847074 (Pubitemid 34183104)
33. Control Of stationary behavior in probabilistic Boolean networks by means Of structural intervention. Shmulevich I, Dougherty ER, Zhang W, J Biol Syst 2002 10 4 431 445 10.1142/S0218339002000706 (Pubitemid 135712413)
34. Intervention in a family of Boolean networks. Choudhary A, Datta A, Bittner ML, Dougherty ER, Bioinformatics 2006 22 2 226 232 10.1093/ bioinformatics/bti765 16286362 (Pubitemid 43205411)
35. Stewart WJ, Introduction to the Numerical Solution of Markov Chains Princeton NJ: Princeton University Press 1994
36. Simulation study in probabilistic Boolean network models for genetic regulatory networks. Zhang S, Ching W, Ng MK, Akutsu T, Int J Data Mining Bioinf 2007 1 3 217 240 10.1504/IJDMB.2007.011610
37. A matrix perturbation method for computing the steady-state probability distributions of probabilistic Boolean networks with gene perturbations. Xu W, Ching W, Zhang S, Li W, Xiao-ShanChen, J Comput Appl Math 2011 235 2242 2251 10.1016/j.cam.2010.10.021
38. Berg BA, Markov Chain Monte Carlo Simulations and Their Statistical Analysis Singapore: World Scientific Publishing 2004
39. Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo. Rosenthal JS, J Am Stat Assoc 1995 90 430 558 566 10.1080/01621459.1995. 10476548
40. Convergence control techniques for Markov chain Monte Carlo algorithms. Robert CP, Stat Sci 1995 10 3 231 253 10.1214/ss/1177009937
41. Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review. Cowles MK, Carlin BP, J Am Stat Assoc 1996 91 434 883 904 10.1080/01621459.1996. 10476956
42. How Many Iterations in the Gibbs Sampler? Raftery AE, Lewis S, Bayesian Statistics, Volume 4 Oxford UK: Oxford University Press, Bernardo JM, Berger JO, Dawid AP, Smith AFM, 1992 763 773
43. Noisy attractors and ergodic sets in models of gene regulatory networks. Ribeiro AS, Kauffman SA, J Theor Biol 2007 247 4 743 755 10.1016/j.jtbi.2007.04. 020 17543998 (Pubitemid 47081056)
44. Ergodic Sets as cell phenotype of budding yeast cell cycle. Todd R, Helikar T, PLOS One 2012 7 10 45780 10.1371/journal.pone.0045780 23049686
45. Analyzing steady state probability distributions of context-sensitive probabilistic Boolean networks. Pal R, Proc. IEEE, International Workshop on Genomic Signal Processing and Statistics (GENSIPS): 17-21 May; Minneapolis, MN, USA IEEE Computer Society 2009 1 4
46. Context-Sensitive Probabilistic Boolean networks: steady-state properties, reduction, and steady-state approximation. Pal R, IEEE Trans Signal Process 2010 58 2 879 890
47. Distribution and enumeration of attractors in probabilistic Boolean networks. Hayashida M, Tamura T, Akutsu T, Ching W, Cong Y, IET Syst Biol 2009 3 6 465 474 10.1049/iet-syb.2008.0177 19947772
48. Stability and Stabilization Issue of Probabilistic Boolean Network. Li F, Sun J, Proc. 30th Chinese Control Conference: 22-24 July; Yantai, China IEEE Computer Society 2011 6381 6385
49. Homomorphisms of probabilistic gene regulatory networks. Avi nó M A, Proc. IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS): 28-30 May; College Station, Texas, USA IEEE Computer Society 2006 85 86
50. Generating Boolean networks with a prescribed attractor structure. Pal R, Ivanov I, Datta A, Bittner ML, Dougherty ER, Bioinformatics 2005 21 21 4021 4025 10.1093/bioinformatics/bti664 16150807 (Pubitemid 41631844)
51. Compression-based discretization of continuous attributes. Pfahringer B, Proc. 12th International Conference on Machine Learning: 9-12 July; Tahoe City, CA, USA Morgan Kaufmann Publishers 1995 456 463
52. Supervised and unsupervised discretization of continuous features. Dougherty J, Kohavi R, Sahami M, Proc. 12th International Conference on Machine Learning: 9-12 July; Tahoe City, CA, USA Morgan Kaufmann Publishers 1995 194 202
53. Quantization and similarity measure selection for discrimination of lymphoma subtypes under k-nearest neighbor classification. Mircean C, Tabus I, Astola J, Kobayashi T, Shiku H, Yamaguchi M, Shmulevich I, Zhang W, B Y, Microarrays and Combinatorial Techniques: Design, Fabrication, and Analysis, Volume 5328 of Proceedings of the SPIE: January; San Jose, CA, USA 2004 6 17 BiOS
54. Qualitative dynamics of piecewise-linear differential equations: a discrete mapping approach. Snoussi EH, Dyn and Stability Syst 1989 4 3-4 189 207
55. Gersho A, Gray RM, Vector Quantization and Signal Compression Boston Kluwer Academic Publishers 1992
56. Binary analysis and optimization-based normalization of gene expression data. Shmulevich I, Zhang W, Bioinformatics 2002 18 4 555 565 10.1093/bioinformatics/18.4.555 12016053 (Pubitemid 34521045)
57. Binarization of microarray data on the basis of a mixture model. Zhou X, Wang X, Dougherty ER, Mol Cancer Ther 2003 2 7 679 684 12883041
58. Multiscale binarization of gene expression data for reconstructing Boolean networks. Hopfensitz M, Müssel C, Wawra C, Maucher M, Kühl M, Neumann H, Kestler HA, IEEE/ACM Trans Comput Biol Bioinf 2012 9 2 487 498
59. Activation detection on fMRI time series using hidden Markov model. Duan R, Man H, Jiang W, Liu W, Proc. 2nd International IEEE EMBS Conference on Neural Engineering: 16-19 March; Arlington, VA, USA IEEE Computer Society 2005 510 513 (Pubitemid 43803372)
60. Computational limitations on learning from examples. Pitt L, Valiant LG, J ACM 1988 35 965 984 10.1145/48014.63140
61. Error-free and best-fit extensions of partially defined Boolean functions. Boros E, Ibaraki T, Makino K, Inf Comput 1998 140 2 254 283 10.1006/inco.1997.2687 (Pubitemid 128453343)
62. On learning gene regulatory networks under the Boolean network model. Lähdesmäki H, Shmulevich I, Yli-Harja O, Mach Learn 2003 52 147 167 10.1023/A:1023905711304
63. Hub-centered gene network reconstruction using automatic relevance determination. Böck M, Ogishima S, Tanaka H, Kramer S, Kaderali L, PloS ONE 2012 7 5 35077 10.1371/journal.pone.0035077 22570688
64. Inference of Boolean networks using sensitivity regularization. Liu W, Lähdesmäki H, Dougherty ER, Shmulevich I, EURASIP J Bioinf Syst Biol 2008 2008 780541
65. The hardwiring of development: organization and function of genomic regulatory systems. Arnone MI, Davidson EH, Development 1997 124 10 1851 1864 9169833 (Pubitemid 27270255)
66. Modeling gene expression with differential equations. Chen T, He HL, Church GM, Proceedings of the Pacific Symposium on Biocomputing, Volume 4: 4-9 January; Hawaii, USA 1999 29 40 World Scientific Press
67. Searching for limited connectivity in genetic network models. Someren EPV, Wessels LFA, Reinders MJT, Backer E, Proc. 2nd International Conference on Systems Biology: 4-7 November; Pasadena, CA, USA Omnipress 2001 222 230
68. Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Guthke R, Möller U, Hoffmann M, Thies F, Töpfer S, Bioinformatics 2005 21 8 1626 1634 10.1093/ bioinformatics/bti226 15613398 (Pubitemid 40542721)
69. The large-scale organization of metabolic networks. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási A, Nature 2000 407 651 654 10.1038/35036627 11034217
70. Murphy K, Mian S, Modelling Gene expression Data Using Dynamic Bayesian Networks Tech. Rep., Berkeley CA: University of California Press 1999
71. Gene networks inference using dynamic Bayesian networks. Perrin B, Ralaivola L, Mazurie A, Bottani S, Mallet J, d'Alché-Buc F, Bioinformatics 2003 19 Suppl. 2 i138 ii148 14534183
72. Overview of computational methods for the inference of gene regulatory networks. Styczynski MP, Stephanopoulos G, Comput Chem Eng 2005 29 519 534 10.1016/j.compchemeng.2004.08.029 (Pubitemid 40417800)
73. Could correlation-based methods be used to derive genetic association networks? Lindlöf A, Olsson B, Inf Sci 2002 146 1-4 103 113 10.1016/S0020-0255(02)00218-9 (Pubitemid 35214294)
74. Inferring Boolean network structure via correlation. Maucher M, Kracher B, M Kühl M, Kestler HA, Bioinformatics 2011 27 11 1529 1536 10.1093/bioinformatics/btr166 21471013
75. Optimizing consistency-based design of context-sensitive gene regulatory networks. Xiao Y, Dougherty E, IEEE Trans Circuits and Syst 2006 53 11 2431 2437 (Pubitemid 44824321)
76. Coeffcient of determination in nonlinear signal processing. Dougherty ER, Kim S, Chen Y, Signal Process 2000 80 2219 2235 10.1016/S0165-1684(00)00079-7
77. On construction of stochastic genetic networks based on gene expression sequences. Ching W, Ng MM, Fung ES, Akutsu T, Int J Neural Syst 2005 15 4 297 310 10.1142/S0129065705000256 16187405 (Pubitemid 44099497)
78. A heuristic method for generating probabilistic Boolean networks from a Prescribed Transition Probability Matrix. Ching W, Chen X, Tsing N, Leung H, Proc. 2nd Symposium on Optimization and Systems Biology (OSB): 31 October-3 November; Ligiang, China World Publishing Corporation 2008 271 278
79. Design of probabilistic Boolean networks under the requirement of contextual data consistency. Dougherty ER, Xiao Y, IEEE Trans Signal Process 2006 54 9 3603 3613 (Pubitemid 46405396)
80. A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks. Zhou X, Wang X, Pal R, Ivanov I, Bittner M, Dougherty ER, Bioinformatics 2004 20 17 2918 2927 10.1093/bioinformatics/bth318 15145802 (Pubitemid 39619183)
81. Modelling by shortest data description. Rissanen J, Automatica 1978 14 465 471 10.1016/0005-1098(78)90005-5 (Pubitemid 9397141)
82. Inferring gene regulatory networks from time series data using the minimum description length principle. Zhao W, Serpedin E, Dougherty ER, Bioinformatics 2006 22 17 2129 2135 10.1093/bioinformatics/btl364 16845143 (Pubitemid 44390904)
83. Inference of gene regulatory networks based on a universal minimum description length. Dougherty J, Tabus I, Astola J, EURASIP J Bioinf Syst Biol 2008 2008 482090
84. Temporal inference of probabilistic Boolean networks. Marshall S, Yu L, Xiao Y, Dougherty ER, Proc. IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS): 28-30 May; College Station, Texas, USA IEEE Computer Society 2006 71 72
85. Inference of a probabilistic Boolean network from a single observed temporal sequence. Marshall S, Yu L, Xiao Y, Dougherty ER, EURASIP J Bioinf Syst Biol 2007 2007 32454
86. Optimal state estimation for Boolean dynamical systems. Braga-Neto U, Proc. 45th Annual Asilomar Conference on Signals, Systems, and Computers: 6-9 November; Pacific Grove, CA, USA IEEE Computer Society 2011 P8a4 5
87. Can Markov chain models mimic biological regulation? Kim S, Li H, Dougherty ER, Cao N, Chen Y, Bittner M, Suh EB, J Biol Syst 2002 10 4 337 357 10.1142/S0218339002000676 (Pubitemid 135712409)
88. Modeling genetic regulatory networks with probabilistic Boolean networks. Shmulevich I, Dougherty ER, Genomic Signal Processing and Statistics Cairo Egypt: Hindawi Publishing Corporation 2005
89. Inferring connectivity of genetic regulatory networks using information-theoretic criteria. Zhao W, Serpedin E, Dougherty ER, IEEE/ACM Trans Comput Biol Bioinf 2008 5 2 262 274
90. Construction of genomic networks using mutual-information clustering and reversible-jump Markov-chain-Monte-Carlo predictor design. Zhou X, Wang X, Dougherty ER, Signal Process 2003 83 745 761 10.1016/S0165-1684(02)00469-3
91. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Bittner M, Meltzer P, Chen Y, Jiang Y, Sefftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, Nature 2000 406 6795 536 540 10.1038/35020115 10952317 (Pubitemid 30625740)
92. Generating probabilistic Boolean networks from a prescribed stationary distribution. Zhang S, Ching W, Chen X, Tsing NK, Inf Sci 2010 180 2560 2570 10.1016/j.ins.2010.03.014
93. A modified Newton's method for inverse problem of probabilistic Boolean networks with gene perturbations. Li W, Ching W, Cui L, Proc. IEEE International Conference on Systems Biology (ISB): 28 August-1 September; Heidelberg-Mannheim, Germany Curran Associates, Inc 2011 167 172
94. The impact of function perturbations in Boolean networks. Xiao Y, Dougherty ER, Bioinformatics 2007 23 10 1265 1273 10.1093/bioinformatics/btm093 17379691 (Pubitemid 47050543)
95. Effect of function perturbation on the steady-state distribution of genetic regulatory networks: Optimal structural intervention. Qian X, Dougherty ER, IEEE Trans Signal Process 2008 56 10-1 4966 4975
96. Structural intervention of gene regulatory networks by general rank-k matrix perturbation. Qian X, Yoon B, Dougherty ER, Proc. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing: 25-30 March 2013 Kyoto, Japan IEEE Computer Society 2012 729 732
97. Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. Sherman J, Morrison WJ, Ann Math Stat 1950 21 124 127 10.1214/aoms/1177729893
98. On the long-run sensitivity of probabilistic Boolean networks. Qian X, Dougherty ER, J Theor Biol 2009 257 4 560 577 10.1016/j.jtbi.2008.12.023 19168076
99. Bellman R, Dynamic Programming Mineola NY: Dover Publications 2004
100. External control in Markovian genetic regulatory networks. Datta A, Choudhary A, Bittner ML, Dougherty ER, Mach Learn 2003 52 1-2 169 191
101. External control in Markovian genetic regulatory networks: the imperfect information case. Datta A, Choudhary A, Bittner ML, Dougherty ER, Bioinformatics 2004 20 6 924 930 10.1093/bioinformatics/bth008 14751971 (Pubitemid 38559734)
102. Control of Boolean networks Haridness results and algorithms for tree structured networks. Akutsu T, Hayashida M, Ching W, Ng M, J Theor Biol 2007 244 4 670 679 10.1016/j.jtbi.2006.09.023 17069859 (Pubitemid 46148979)
103. Finding optimal control policy by dynamic programming in conjunction with state reduction. Chen X, Ching W, Proc. IEEE International Conference on Systems Biology: 2-4 September 2011; Zhuhai, China IEEE Computer Society 2011 274 278
104. A CoD-based reduction algorithm for designing stationary control policies on Boolean networks. Ghaffari N, Ivanov I, Qian X, Dougherty ER, Bioinformatics 2010 26 12 1556 1563 10.1093/bioinformatics/btq225 20421196
105. State reduction for network intervention in probabilistic Boolean networks. Qian X, Ghaffari N, Ivanov I, Dougherty ER, Bioinformatics 2010 26 24 3098 3104 10.1093/bioinformatics/btq575 20956246
106. An integer programming approach to control problems in probabilistic Boolean networks. Kobayashi K, Hiraishi K, Proc. American Control Conference (ACC): 30 June-2 July; Baltimore, MD, USA American Automatic Control Council 2010 6710 6715
107. Optimal control of context-sensitive probabilistic Boolean networks using integer programming. Kobayashi K, Hiraishi K, Proc. 49th IEEE Conference on Decision and Control (CDC): 15-17 December; Atlanta, Georgia, USA IEEE 2010 7507 7512
108. An integer programming approach to optimal control problems in context-sensitive probabilistic Boolean networks. Kobayashi K, Hiraishi K, Automatica 2011 47 6 1260 1264 10.1016/j.automatica.2011.01.035
109. Optimal control of probabilistic Boolean networks using polynomial optimization. Kobayashi K, Hiraishi K, IEICE Trans 2012 95-A 9 1512 1517
110. Optimal control policy for probabilistic Boolean networks with hard constraints. Ching W, Zhang S, Jiao Y, Akutsu T, Tsing N, Wong A, IET Syst Biol 2009 3 2 90 99 10.1049/iet-syb.2008.0120 19292563
111. On finite-horizon control of genetic regulatory networks with multiple hard-constraints. Cong Y, Ching W, Tsing N, Leung H, BMC Syst Biol 2010 4 Suppl 2 article S14
112. Proc. IEEE International Conference on Bioinformatics and Biomedicine: 18-21 December; Hong Kong, China. Chen X, Akutsu T, Tamura T, Ching W, IEEE Computer Society 2010 240 246
113. Optimal control for probabilistic Boolean networks. Liu Q, Guo X, Zhou T, IET Syst Biol 2010 4 2 99 107 10.1049/iet-syb.2009.0006 20232990
114. Controllability of probabilistic Boolean control networks. Li F, Sun J, Automatica 2011 47 12 2765 2771 10.1016/j.automatica.2011.09.016
115. Optimal infinite horizon control for probabilistic Boolean networks. Pal R, Datta A, Dougherty ER, IEEE Trans Signal Process 2006 54 6 2375 2387 (Pubitemid 43805580)
116. Robust intervention in probabilistic Boolean networks. Pal R, Datta A, Dougherty ER, IEEE Trans Signal Process 2008 56 3 1280 1294 (Pubitemid 351386284)
117. Intervention in gene regulatory networks via a stationary mean-first-passage-time control policy. Vahedi G, Faryabi B, Chamberland J, Datta A, Dougherty ER, IEEE Trans Biomed Eng 2008 55 10 2319 2331 18838357
118. Intervention in gene regulatory networks via greedy control polices based on long-run behavior. Qian X, Ivanov I, Ghaffari N, Dougherty ER, BMC Syst Biol 2009 3: article 61
119. Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle. Faure A, Naldi A, Chaouiya C, Theiffry D, Bioinformatics 2006 22 14 124 131 10.1093/bioinformatics/btl210 16267082 (Pubitemid 44288280)
120. Optimal intervention strategies for cyclic therapeutic methods. Vahedi G, Faryabi B, Chamberland J, Datta A, Dougherty ER, IEEE Trans Biomed Eng 2009 56 2 281 291 19272947
121. Optimal intervention strategies for therapeutic methods with fixed-length duration of drug effectiveness. Yousefi MR, Datta A, Dougherty ER, IEEE Trans Signal Process 2012 60 9 4930 4944
122. Intervention in gene regulatory networks via phenotypically constrained control policies based on long-run behavior. Qian X, Dougherty ER, IEEE/ACM Trans Comput Biol Bioinf 2012 9 123 136
123. Using Bayesian networks to analyze expression data. Friedman N, Linial M, Nachman I, Peér D, J Comput Biol 2000 7 3-4 601 620 11108481
124. Applying dynamic Bayesian networks to perturbed gene expression data. Dojer N, Gambin A, Mizera A, Wilczyński B, Tiuryn J, BMC Bioinformatics 2006 7 249 10.1186/1471-2105-7-249 16681847 (Pubitemid 44110371)
125. From gene expression to gene regulatory networks in Arabidopsis thaliana. Needham C, Manfield I, Bulpitt A, Gilmartin P, Westhead D, BMC Syst Biol 2009 3 85 1 17 19118495
126. Inferring cell cycle feedback regulation from gene expression data. Ferrazzi F, Engel F, Wu E, Moseman A, Kohane I, Bellazzi R, Ramoni M, J Biomed Inf 2011 44 565 575 10.1016/j.jbi.2011.02.002
127. Inferring causal relationships among different histone modifications and gene expression. Yu H, Zhu S, Zhou B, Xue H, Han J, Genome Res 2008 18 1314 1324 10.1101/gr.073080.107 18562678
128. Systems analysis of EGF receptor signaling dynamics with Micro-Western arrays. Ciaccio M, Wagner J, Chuu C, Lauffenburger D, Jones R, Nature Methods 2010 7 2 148 155 10.1038/nmeth.1418 20101245
129. Inference of an in situ epidermal intracellular signaling cascade. Cursons J, Hurley D, Angel C, Dunbar R, Crampin E, Jacobs M, Proc. 32nd Annual International Conference of the IEEE EMBS: 1-4 September; Buenos Aires, Argentina IEEE Computer Society 2010 799 802
130. A Comprehensive statistical model for cell signaling. Yoeruek E, Ochs M, Geman D, Younes L, IEEE/ACM Trans Comput Biol Bioinf 2011 8 3 592 606
131. Bayesian Network approach to understand regulation of biological processes in Cyanobacteria. Elvitigala T, Singh A, Pakrasi H, Ghosh B, Proc. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference: 15-18 December; Shanghai, China IEEE Computer Society 2009 3739 3744
132. Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the Intensive Care Unit. Peelen L, De Keizer N, De Jonge E, Bosman R, Abu-Hanna A, Peek N, J Biomed Inf 2010 43 272 286
133. Prediction of Chronic Obstructive Pulmonary Disease (COPD) in Asthma Patients using electronic medical records. Himes B, Dai Y, Kohane I, Weiss S, Ramoni M, J Am Med Inf Assoc 2009 16 3 371 379 10.1197/jamia.M2846
134. Bayesian network model for diagnosis of social anxiety disorder. Estabragh Z, Kashani M, Moghaddam F, Sari S, Oskooyee K, Proc. IEEE International Conference on Bioinformatics and Biomedicine Workshops: 12-15 Noverber; Atlanta, GA, USA IEEE Computer Society 2011 639 640
135. Enhancing the role of veterinary vaccines reducing zoonotic diseases of humans: Linking systems biology with vaccine development. Adams L, Khare S, Lawhon S, Rossetti C, Lewin H, Lipton M, Turse J, Wylie D, Bai Y, Drake K, Vaccine 2011 29 7197 7206 10.1016/j.vaccine.2011.05.080 21651944
136. Using Bayesian networks and importance measures to indentify tumour markers for breast cancer. Si S, Liu G, Cai Z, Xia P, Proc. IEEE Conference on Industrial Engineering and Engineering Management (IEEM): 6-9 December; Singapore IEEE Computer Society 2011 1826 1830
137. Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks. Lähdesmäki H, Hautaniemi S, Shmulevich I, Yli-Harja O, Signal Process 2006 86 814 834 10.1016/j.sigpro.2005.06.008 (Pubitemid 43242976)
138. Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks. Li P, Zhang C, Perkins E, Gong P, Deng Y, BMC Bioinformatics 2007 8 Suppl 7 13 10.1186/1471-2105-8-S7-S13 18047712 (Pubitemid 352015091)
139. Probabilistic logic method and some applications to biology and medicine. Sakhanenko N, Galas D, J Comput Biol 2012 19 3 316 336 10.1089/cmb.2011.0234 22401592
140. Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery. Huang S, J Mol Med 1999 77 6 469 480 10.1007/s001099900023 10475062 (Pubitemid 29372579)
141. Early detection of cancer by reguression analysis and computer simulation of gene regulatory rules. Nguyen D, Azadivar F, Proc. IEEE International Conference on Biomedical Engineering (ICoBE): 27-28 February; Penang, Malaysia IEEE Computer Society 2012 144 148
142. Stochastic model of yeast cell-cycle network. Zhang Y, Quian M, Ouyang Q, Deng M, Li F, Tang C, Physica D 2006 209 35 39 (Pubitemid 44015933)
143. Reconstructing signaling pathways from RNAi data using Probabilistic Boolean threshold networks. Kaderali L, Dazert E, Zeuge U, Frese M, Bartenschlager R, Bioinformatics 2009 25 17 2229 2235 10.1093/bioinformatics/ btp375 19542154
144. Metabolic networks in motion: 13C-based flux analysis. Sauer U, Mol Syst Biol 2006 62 1 10
145. Holland J, Adaptation in Natural and Artificial Systems Cambridge MA: MIT Press 1992
146. ON/OFF and beyond-a Boolean model of Apoptosis. Schlatter R, Schmich K, Vizcarra IA, Scheurich R, Sauter R, Borner C, Ederer M, Merfort I, Sawodny O, PLOS Comput Biol 2009 5 12 1000595 10.1371/journal.pcbi.1000595 20011108
147. Bridging the layers: towards integration of signal transduction, regulation and metabolism into mathematical models. Gonçalves E, Bucher J, Ryll A, Niklas J, Mauch K, Klamt S, Rocha M, Saez-Rodriguez J, Mol Biosyst 2013 9 7 1576 83 10.1039/c3mb25489e 23525368
148. BMC Syst Biol. Terfve C, Cokelaer T, Henriques D, MacNamara A, Goncalves E, Morris M, Van Lersel M, Lauffenburger D, Saez-Rodriguez J, 2012 133 133
149. The human genome project: lesson from large-scale biology. Collins F, Morgan M, Patrinos A, Science 2003 300 5617 286 290 10.1126/science.1084564 12690187 (Pubitemid 36433711)
150. DNA sequencing-clinical applications of new DNA sequencing technologies. Dewey F, Pan S, Wheeler M, Quake S, Ashley E, Circulation 2012 125 931 944 10.1161/CIRCULATIONAHA.110.972828 22354974
151. Translational bioinformatics and healthcare informatics: computational and ethical challenges. Sethi P, Theodos K, Perspect in Health Inf Manage 2009 6 1h
152. A knowledge model for analysis and simulation of regulatory networks. Rzhetsky A, Koike T, Kalachikov S, Gomez SM, Krauthammer M, Kaplan SH, Kra P, Russo JJ, Friedman C, Bioinformatics 2000 16 12 1120 1128 10.1093/ bioinformatics/16.12.1120 11159331 (Pubitemid 32112005)
153. Optimal constrained stationary intervention in gene regulatory networks. Faryabi B, Vahedi G, Chamberland J, Datta A, Dougherty E, EURASIP J Bioinf Syst Biol 2008 620767 1 10
154. The human disease network. Goh K, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL, Proc Nat Acad Sci 2007 104 21 8685 8690 10.1073/pnas. 0701361104 17502601 (Pubitemid 47175372)
155. Principles and practice of function MRI of the human brain. Gore J, J Clin Invest 2003 112 1 4 9 12840051 (Pubitemid 38056367)
156. A review of Parkinson's disease. Davie C, British Med Bull 2008 86 1 109 127 10.1093/bmb/ldn013 (Pubitemid 351809630)
157. Diseases as network perturbations. Del Sol A, Balling R, Hood L, Galas D, Curr Opin Biotechnol 2010 21 566 571 10.1016/j.copbio.2010.07.010 20709523
158. Del Moral P, Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications Berlin Germany: Springer 2004
159. Modeling genetic algorithms with interacting particle systems. Moral PD, Kallel L, Rowe J, Theoretical Aspects of Evolutionary Computing Berlin Germany: Springer 2001 10 67
160. Branching and interacting particle systems approximations of feynman-kac formulae with applications to non-linear filtering. Moral P, Miclo L, Seminaire de Probability XXXIV, Volume 1729 of Lecture Notes in Mathematics Berlin Germany: Springer 2000 1 145
161. Handbook of Evolutionary Computation London UK: IOP Publishing Ltd., Michalewicz Z Z, Michalewicz Z, 1997
162. Jong KAD, Evolutionary Computation: A Unified Approach Cambridge MA: MIT Press 2006
163. Eiben AE, Smith JE, Introduction to Evolutionary Computing Berlin Germany: Springer 2003
164. Ashlock DA, Evolutionary Computation for Modeling and Optimization Berlin Germany: Springer 2006
165. An overview of evolutionary computation. Spears W, Jong K, Bäck T, Fogel D, Garis H, Machine Learning: ECML-93, Volume 667 of Lecture Notes in Computer Science Berlin Germany: Springer 1993 442 459